Subject matter expert knowledge mapping using dynamic clustering

ABSTRACT

The example embodiments are directed to a system and method that applies knowledge developed by a subject matter expert with respect to a physical asset. In one example, the method includes receiving knowledge and issue resolution information developed of subject matter experts in association with historical issues for an asset, generating a plurality of data clusters for the asset based on the knowledge, wherein each historical issue of the asset is mapped to a cluster and includes a plurality of resolutions for the issue, receiving a new issue and new issue information, and processing the new issue by extracting keywords from the new issue information and assigning the new issue to a data cluster from among the plurality of data clusters based on the extracted keywords, and outputting, to a display, a cause of the new issue and potential solutions for the new issue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/659,879 filed on Jul. 26, 2017, in the United States Patent andTrademark Office, the entire disclosure of which is hereby incorporatedherein for all purposes.

BACKGROUND

Machine and equipment assets, generally, are engineered to performparticular tasks as part of a business process. For example, assets caninclude, among other things and without limitation, industrialmanufacturing equipment on a production line, drilling equipment for usein mining operations, wind turbines that generate electricity on a windfaun, transportation vehicles such as trains and aircraft, and the like.As another example, assets may include devices that aid in diagnosingpatients such as imaging devices (e.g., X-ray or MRI systems),monitoring equipment, and the like. The design and implementation ofthese assets often takes into account both the physics of the task athand, as well as the environment in which such assets are configured tooperate.

Low-level software and hardware-based controllers have long been used todrive machine and equipment assets. However, the rise of inexpensivecloud computing, increasing sensor capabilities, and decreasing sensorcosts, as well as the proliferation of mobile technologies have createdopportunities for creating novel industrial and healthcare based assetswith improved sensing technology and which are capable of transmittingdata that can then be distributed throughout a network. As aconsequence, there are new opportunities to enhance the business valueof some assets through the use of novel industrial-focused hardware andsoftware.

The machines and equipment that are installed in a manufacturingenvironment are often quite big in size and/or complex in theiroperations. Issues in these machines can also be quite complex andrequire the service of highly skilled engineers, also referred to hereinas subject matter experts. When the subject matter expert performing awork order is new to the job or is not highly skilled, it can takehours, or even longer, for the expert to resolve the issues. Forexample, the subject matter expert typically analyzes dozens if nothundreds of sources of information, evaluates the information, and makesa best guess as to the issues and reasons for error/failure associatedwith the particular asset. In order to make such a determination, asubject matter expert often analyzes textual based data such as repairorders, work orders, service orders, notes made by engineers/techniciansin the field, materials used, and the like. After analyzing all of thisdata, a subject matter expert then makes the best-guess as to the causeof an asset failure.

SUMMARY

Embodiments described herein improve upon the prior art by providingsystems and methods which capture the knowledge of a subject matterexpert with respect to an asset and automatically apply that knowledgeto issues with the asset to assist or even take the place of the subjectmatter expert. For example, the subject matter expert knowledge may becaptured from various opinions and solutions issued by the subjectmatter expert. The sources of knowledge may include repair orders, workorders, service requests, parts usage, part orders, and the like, whichare used to categorize an event that has occurred with the asset,describe a part or a problem in the asset, or otherwise diagnose orcharacterize a state of the asset. In some examples, the embodimentsherein may be incorporated within software that is deployed on a cloudplatform for use with an Internet of Things (IoT) system.

In an aspect of an example embodiment, a computer-implemented methodthat includes receiving knowledge and issue resolution informationdeveloped by one or more subject matter experts in association withhistorical issues for a type of asset, generating a plurality of dataclusters for the type of asset based on the received knowledge, whereineach data cluster is mapped to a historical issue with the asset andincludes a plurality of resolutions for the issue, receiving a new issueincluding new issue information for the type of asset, and processingthe new issue by extracting keywords from the new issue information andassigning the new issue to a data cluster from among the plurality ofdata clusters based on the extracted keywords, and outputting, to adisplay, a cause of the issue corresponding to the assigned data clusterand a ranking of a plurality of resolutions for a historical issuecorresponding to the assigned data cluster as potential solutions forthe new issue.

In an aspect of another example embodiment, a computing system includesa network interface configured to a network interface configured toreceive knowledge and issue resolution information developed by one ormore subject matter experts in association with historical issues for atype of asset, and to receive a new issue including new issueinformation for the type of asset, a processor configured to generate aplurality of data clusters for the type of asset based on the receivedknowledge, wherein each data cluster is mapped to a historical issuewith the asset and includes a plurality of resolutions for thehistorical issue, and to further process the new issue by extractingkeywords from the new issue information and assigning the new issue to adata cluster from among the plurality of data clusters based on theextracted keywords, and an output configured to output, to a display, acause of the issue corresponding to the assigned data cluster and aranking of a plurality of resolutions for a historical issuecorresponding to the assigned data cluster as potential solutions forthe new issue.

Other features and aspects may be apparent from the following detaileddescription taken in conjunction with the drawings and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the example embodiments, and the manner inwhich the same are accomplished, will become more readily apparent withreference to the following detailed description taken in conjunctionwith the accompanying drawings.

FIG. 1 is a diagram illustrating a cloud computing environment forcapturing and applying a subject matter opinion in accordance with anexample embodiment.

FIG. 2 is a diagram illustrating a process for capturing and applyingknowledge from a subject matter expert in accordance with an exampleembodiment.

FIGS. 3A and 3B are diagrams illustrating a process of clusteringknowledge from a subject matter expert in accordance with an exampleembodiment.

FIG. 4 is a diagram illustrating a method of applying subject matterexpert knowledge in accordance with an example embodiment.

FIG. 5 is a diagram illustrating a computing system for applying subjectmatter expert knowledge in accordance with an example embodiment.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated or adjusted forclarity, illustration, and/or convenience.

DETAILED DESCRIPTION

In the following description, specific details are set forth in order toprovide a thorough understanding of the various example embodiments. Itshould be appreciated that various modifications to the embodiments willbe readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other embodiments andapplications without departing from the spirit and scope of thedisclosure. Moreover, in the following description, numerous details areset forth for the purpose of explanation. However, one of ordinary skillin the art should understand that embodiments may be practiced withoutthe use of these specific details. In other instances, well-knownstructures and processes are not shown or described in order not toobscure the description with unnecessary detail. Thus, the presentdisclosure is not intended to be limited to the embodiments shown, butis to be accorded the widest scope consistent with the principles andfeatures disclosed herein.

As referred to herein, a subject matter expert is someone withsubjective experience with a subject or topic, for example, a field oftechnology, a type of machine, an area of study, and the like, and mayinclude a professional in the field, an engineer, a technician, or thelike. Subject matter experts are always in high demand especially in theareas of machine and equipment repair/failure diagnosis. However, when asubject matter expert leaves an organization or moves to a differentposition within the organization, the organization is now devoid of theknowledge of that expert. Furthermore, an expert can take hours or evendays to analyze different sources of information before making aneducated guess as to the cause of an issue with a machine-based or anequipment-based asset.

The example embodiments provide a knowledge mapping software (e.g.,application, service, program, code, etc.) and a system that mapshistoric knowledge of a subject matter experts to the system. As aresult, when a new order related to an issue with an asset is receivedby the system, and a user (e.g., a service engineer) needs to work onthe order, the user can be provided with a list possible fixes to assistthe user or even take the place of the user. The possible fixes arebased on previous actions taken by subject matter experts to resolvesimilar work orders. Alongside the possible fixes, the user may also beprovided with similar work orders that have been raised in the past andwho resolved the issue related thereto. By providing the user with thepotential fix information, work order resolution time can besignificantly reduced. Furthermore, the tool aggregates information forresolving the work orders into a single system creating a knowledge basefor users.

In various embodiments, the subject matter may include industrial and/ormanufacturing based equipment, machines, devices, etc., and may includehealthcare machines, industrial machines, manufacturing machines,chemical processing machines, textile machines, locomotives, aircraft,energy-based machines, oil rigs, and the like. The knowledge mappingsoftware may analyze historical issues and resolution information (e.g.,work orders, repair notes, parts lists, etc.) generated by a subjectmatter expert or generated in association with an issue of the asset,and cluster the historical issues into various meaningful clusters. Inthis case, the subject matter expert knowledge can be mapped to aplurality of clusters. For example, a plurality of occurrences of a sameissue may be mapped to a same cluster to identify various keywords,phrases, etc. that are used by the subject matter expert during theidentification and resolution of the issue. In some cases, the clusteredresults can be further refined based on feedback from the subject matterexpert themselves to eliminate any anomalies and further refine thelearning. Based on the clustered data, the application can analyze asame type of data or a different type of data to generate a subjectmatter expert opinion of the data.

Recently, there has been a huge improvement in clustering algorithms.These algorithms help in clustering data into meaningful clusters.However, when there is a lot of noise in the data (very common in workorder, service order, feedback info, etc.), clustering technique cangenerate a lot of redundant clusters. The gap in the clusteringalgorithms is resolved by the knowledge mapping algorithm providedherein. Furthermore, the clustered knowledge of the subject matterexpert can also be used to determine a cause and a resolution for a newissue with the asset.

The knowledge mapping software may be deployed on a cloud platformcomputing environment, for example, an Internet of Things (IoT) or anIndustrial Internet of Things (IIoT) based platform. While progress withmachine and equipment automation has been made over the last severaldecades, and assets have become ‘smarter,’ the intelligence of anyindividual asset pales in comparison to intelligence that can be gainedwhen multiple smart devices are connected together, for example, in thecloud. Assets, as described herein, may refer to equipment and machinesused in fields such as energy, healthcare, transportation, heavymanufacturing, chemical production, printing and publishing,electronics, textiles, and the like. Aggregating data collected from orabout multiple assets can enable users to improve business processes,for example by improving effectiveness of asset maintenance or improvingoperational performance if appropriate industrial-specific datacollection and modeling technology is developed and applied.

With a projected skill gap of around 2 million workers in themanufacturing industry by 2025, there is a high priority in the industryto motivate more skilled personnel to join the sector. A significantnumber of retirements (around 2.7 million) are also projected by thesame time. This will lead to a scenario with a lot of workers in themanufacturing industry lacking a substantial number of years ofexperience. Furthermore, because manufacturing and service industries gohand in hand, from the above projections it is also likely that theservice industry will be hugely affected as well. In the absence ofexperienced personnel present to resolve complex issues, the work orderresolution time will undoubtedly increase for the new service engineers.

The example embodiments including the knowledge mapping applicationprovide respite to these service engineers. Given that the software andsystem stores the knowledge of work order resolution and providespossible fixes that can be carried out, it makes the life of the newservice engineers easier. Also, the more an SME spends time in refiningthe rules in the system, the more accurate the system can become indetermining the potential fixes for a work order raised. The system isable to reduce the long resolution times which would have otherwiseoccurred in its absence.

FIG. 1 illustrates a cloud-based system 100 for preserving and applyingsubject matter expert knowledge in accordance with an exampleembodiment. In this example, the subject matter expert is associatedwith one or more types of assets. Referring to FIG. 1, the system 100includes a group of assets 110, subject matter expert (SME) data store120, a cloud computing platform (e.g., cloud platform) 130 thatrepresents a cloud-based environment according to various embodiments,and a user device 140. It should be appreciated that the system 100 ismerely an example and may include additional devices and/or one of thedevices shown may be omitted. As another example, the software describedherein may be included on a single device without the interaction of asystem. The cloud computing platform 130 may be one or more of a server,a computer, a database, and the like, included in a cloud-basedplatform. The user device 140 may include a computer, a laptop, atablet, a mobile device, a television, an appliance, a kiosk, and thelike. In the example of FIG. 1, the assets 110, the SME data store 120,and/or the user device 140 may be connected to the cloud platform 130via a network such as the Internet.

An asset 110 may be outfitted with one or more sensors configured tomonitor respective operations or conditions. Data from the sensors canbe recorded or transmitted to the cloud-based or other remote computingenvironment described herein. By bringing such data into a cloud-basedcomputing environment 100, subject matter experts may analyze issuessuch as machine or equipment failure and provide a subjective opinion asto the reason for such failure, part classification, and the like, basedon a totality of evidence (e.g., textual data, sensor data, etc.) frommultiple and different sources. These opinions along with the data usedby the subject matter expert to make such an opinion may be stored inSME data store 120. Insights gained through analysis of such data canlead to enhanced asset designs, enhanced software algorithms foroperating the same or similar assets, better operating efficiency, andthe like. In addition, analytics may be used to analyze, evaluate, andfurther understand issues related to operation of the asset withinmanufacturing and/or industry. However, expert opinions can often take asignificant amount of time because it requires the expert to readthrough significant amounts of data, apply their personal knowledge andsubject matter on the subject matter, and render an opinion.Furthermore, a subject matter expert is not always right. In fact, it isestimated that only 50-60% will two experts agree on the cause of anissue with an asset.

According to various embodiments, the knowledge mapping software learnsfrom the knowledge of a subject matter expert based on historical issuesof an asset the opinions of the subject matter expert with respect tothose historical issues, generates a clustering algorithm based on whatis learned, and applies the clustered knowledge to new issues associatedwith the asset. The knowledge mapping software may be deployed on thecloud computing platform 130. The software may receive new data about anissue with the asset and determine a cause/reason for the issue andpossible resolutions based on the clustered knowledge of the subjectmatter expert without the need for the subject matter expert to becomeinvolved. Accordingly, if the subject matter expert is unavailable forwhatever reason, or merely to supplement a subject matter expert'sopinion, the software described herein may provide an automateddetermination of a cause for an issue as well as a resolution of theissue for an asset and output the determination to a screen of the userdevice 140.

Subject matter expert data with respect to a type of asset such as aparticular machine or equipment used in industry/manufacturing may bestored in SME data store 120. For example, the subject matter expertdata may include the determination of the cause of a failure rendered bythe subject matter expert themselves as well as data related to thefailure which was used by the subject matter expert in rendering theopinion. The related data may not be prepared by the subject matterexpert but may be prepared by others in response to the failure such aswork orders, workshop notes, material purchase orders, repair orders,and the like, with respect to a particular type of asset. The data mayinclude textual based data that is provided when repairing or fixing theasset and which is used by the subject matter expert in making adetermination.

The software application described herein and deployed on the cloudplatform 130 in FIG. 1 may learn from the subject matter expert datastored in the SME data store 120, and generate a subject matter expertknowledge mapping. For example, the historical information provided inconnection with previous with a type of asset may be analyzed andclustered into different failure topics or causes. As will beappreciated, a type of asset (e.g., type of machine or equipment) mayhave hundreds of causes of failure. For example, a healthcare machine ora manufacturing machine may have hundreds of parts and/or software thatneed repair or replacement. Accordingly, there may be hundreds ofclusters of textual data as well as opinion information of the subjectmatter expert for each cause of failure.

The clustering may include clustering textual data from work orders,material orders, repair notes, and the like, into a particular causefrom among a plurality of causes for a type of asset. That is, the causemay be identified by a subject matter expert while the associated datamay be used by the subject matter expert to render the opinion. In someexamples, each cause may correspond to a single cluster, however, theembodiments are not limited thereto. When new failure information of thesame type asset is received, for example, from an asset 110 or a systemassociated with the asset 110, the failure information may processed bythe application deployed on the cloud platform 130 to automaticallydetermine a cause for the failure based on the subject matter knowledgemapping as well as to determine possible resolutions for the failure.

The determined failure may be output to a display screen of the userdevice 140, or another device. For example, the user device 140 (e.g.,computer, mobile device, workstation, tablet, laptop, appliance, kiosk,and the like) may be configured for data communication with the cloudcomputing platform 130. The user device 140 can be used to monitor orcontrol an asset 110, or shipping plans, maintenance plans, repairs, andthe like, related to the asset 110. In an example, information about acause of failure of the asset 110 may be presented to an operator via adisplay of the user device 140. The user device 140 can include optionsand hardware for scheduling repairs and/or parts for the asset 110. Asanother example, the user device 140 may correspond to a device of asubject matter expert themselves. The subject matter expert (via theuser device 140) may remotely connect to the application deployed on thecloud platform 130 and modify the subject matter expert mapping byremoving anomalies or refining particular keywords/phrases to furtherenhance the correctness of the determination.

FIG. 2 illustrates processes for capturing and applying knowledge from asubject matter expert in accordance with an example embodiment.Referring to FIG. 2, process 210 illustrates an example of generatingclusters of knowledge related to issues with an asset, and process 220illustrates an example of applying the clustered knowledge to a newissue with respect to the asset. The process 210 may analyze historicwork order data (with fix information) and dynamically assign clustersto the work orders. To refine the results, the process 210 allows theusers/subject matter experts to add/modify the keywords/features used tocreate the clusters. Alongside, it also determines the ways to resolveany work order.

There are two algorithms which serve as the base for the process 210. In211, the process performs concept detection based on a concept detectionalgorithm which may use multiple steps to determine the key conceptsfrom the dataset. For example, the various steps involved in thisconcept detection process may include preprocessing the input text whichmay include special character removal, stemming, stop words removal, andthe like. In 212, the process determines features and/or keywords fromthe preprocessed input text. For example, the determining in 212 may beperformed based on a feature/keyword extraction algorithm such as arapid automatic keyword extraction (RAKE) algorithm. The RAKE algorithmmay be used to extract keywords from text by identifying runs of wordsthat do not include stop words and then scoring these phrases across thedocument. It requires no training and the only input may be a list ofstop words for a given language, and a tokenizer that splits the textinto sentences and sentences into words.

In 213, the process includes receiving feedback from one or more subjectmatter experts who can add/modify/delete the features/keywords that areused to determine the cluster (the concept) for any work order. Thisenables the clusters to be accurate for any new work order using thefeedback loop from the subject matter expert. In 214, the processinclude final cluster formation based on the extracted features. Forexample, cluster label induction, cluster content discovery, and finalcluster formation may be performed based on the extracted keywords andfeatures (which may be modified and enhanced by a SME). This approachcombines advanced clustering, feature extraction, classification and auser feedback loop. It does so using historical work order data. At thebeginning (while setting up the system), the historical work order datathat has been previously resolved may be uploaded to the system. Thiswill create the rules to determine how any new work order will beassigned to a specific cluster or group of clusters. The subject matterexpert can log on to the system to alter the rules based on his/herexperience. The system ranks the potential resolutions extracted fromthe closed work orders for a cluster. This ranking may be performedbased on the frequency of occurrence of the resolution phrase in thatparticular cluster.

Now, referring to process 220, the process may be used to classify a newissue that has new issue information (e.g., work order, repair order,notes, parts list, etc.) associated with it. When the system receivesthe new issue and the information associated therewith, the system canextract features/keywords from the received information, in 221. Overthe years, there has been a huge improvement in clustering algorithms.These algorithms help in clustering data into meaningful clusters.However, when there is a lot of noise in the data (very common in workorder, service order, feedback info, etc.), clustering technique createsa lot of redundant clusters. We realize this gap in the clusteringalgorithms and hence came up with a knowledge mapping algorithm whichhelps fix this issue. For example, in 222 the knowledge mappingalgorithm can identify which of the predetermined cluster(s) any newwork order falls under. Using historical work order data used for modeldevelopment, and also provide potential resolutions exist for workorders that make up a cluster, in 223.

To provide more options to the end users, we also show, for a given workorder, similar (closed) work orders from the past. This enables theusers to understand the various issues that have been raised in the pastas well as talk to the service personnel who has fixed these issuespreviously. For any new work order, the features/keywords are extractedand based on the rules already forming during the system setup processin 210, the work order is mapped to the appropriate cluster(s) and thepotential fixes are displayed based on the process 220. All theinformation related to work order resolution is thus brought to a singlesystem and the work order fix information is readily provided, therebyenabling quick diagnosis and resolution of work orders.

As shown in these examples, based on the SME's knowledge and clusteredinformation, possible fixes and similar (fixed) work orderscorresponding to any new work order are displayed. The solution ishighly beneficial in resolving any new work orders usinginformation/knowledge mapped onto the system, hence resolution time isreduced. In some embodiments, the input data for a new issue may includetextual data as well as an integration of sensor data from the asset inquestion. For example, sensor data from the same part/machine for whichthe work order has been raised may be analyzed to enable pinpointing ofthe actual issue with the asset and help provide a thorough root causeanalysis to the service engineers.

FIGS. 3A and 3B illustrate examples of metrics provided by the knowledgemapping application at work. These results may be output to a userdevice. In FIG. 3A, user interface 310 illustrates the metrics of workorders at a given plant, factory, location, etc. For example, userinterface 310 includes a work order distribution 312 identifyingdifferent components of an asset that are having issues, machine areas314 identifying machine components that are associated with the issues,and symptoms 316 that are associated with the issues. A work order trend318 also provides an identification of the amount of work orders (i.e.,issues) opened and closed over a predetermined period of time. There isalso an interface within the user interface 310 that allows a user tospecify a range of time of work orders to analyze.

Referring to FIG. 3B, for any selected open work order 3222, userinterface 320 displays corresponding similar work orders 324 along withthe recommended options 326 for fixing the work order. For example,similar work orders may be determined using a cluster identificationmethod for any new work order. Once the cluster or group of clusters aredetermined, the closed work orders present in that cluster or group ofclusters are displayed to the user.

In any factory or other industrial setting, often there are a lot ofmachines installed. Changing/removing any of the machines is typically acost intensive process and results in a huge impact to the businessoperations. To ensure that all the machines are running in the mostoptimal way is therefore a strategic decision which needs to be taken bythe factory manager or operations manager. To make strategic decisionsfor the factory where the machines are installed, business heavilydepends on the most frequently occurring issue in any given time frame.The example embodiments provide not just the traditional metrics butadded multi-level drill downs that will help business make strategicdecisions based on multiple dimensions.

FIG. 4 illustrates a method 400 of applying subject matter expertknowledge in accordance with an example embodiment. For example, themethod 400 may be performed by the knowledge mapping software describedherein and executed on a computing device such as a cloud computingplatform, a user device, a server, or the like. Referring to FIG. 4, in410, the method includes receiving knowledge and issue resolutioninformation developed by one or more subject matter experts inassociation with historical issues for a type of asset. The type ofasset may include a machine or an equipment that is included within anIndustrial Internet of Things (IIoT) network. The subject matter expertmay include a professional, a technician, and an engineer, or the like,who has personal experience on a professional level with the type ofasset. The knowledge received in 410 may be included within documents,files, sensor data, repair notes, work orders, parts lists, and thelike. The issue resolution information may also be included in the samematerials and may identify fixes that were performed for a given issue.The fix information may include the type of fix, the frequency withwhich that fix was performed in comparison to other fixes, and the like.

In 420, the method includes generating a plurality of data clusters forthe type of asset based on the received knowledge. For example, eachhistorical issue may be mapped to data cluster and may include aplurality of resolutions that are available and that have been performedfor that issue. According to various embodiments, the received knowledgefor a historical issue may include text data including words, sentences,paragraphs, etc., and the generating may include scanning the text data,extracting keywords from the scanned text data, and assigning thehistorical issue to the data cluster based on the extracted keywords.For example, the extracting of the keywords may be performed by a rapidautomatic keyword extraction (RAKE) algorithm based on a continuoussequence of words. For example, the method may combine advancedclustering, feature extraction, classification and a user feedback loop.Initially (while setting up the system), the historical issue data(resolved) may be uploaded to the system. The knowledge mapping softwaremay create rules to determine how a new work order is assigned to aspecific cluster or group of clusters. The subject matter expert can logon to the system to alter the rules based on his/her experience. Inaddition, the system can also rank the potential resolutions extractedfrom the closed work orders for a cluster. For example, the ranking maybe performed based on a frequency of occurrence of the resolution phrasein that particular cluster.

In 430, the method includes receiving a new issue including new issueinformation for the type of asset, and processing the new issue byextracting keywords from the new issue information and assigning the newissue to a data cluster from among the plurality of data clusters basedon the extracted keywords. In this example, for any new work order, thefeatures and keywords may be extracted therefrom and based on the rulesalready formed during the system setup, the work order may be mapped toan appropriate clusters and the potential fixes displayed. All theinformation related to work order resolution is thus brought to a singlesystem where the work order fix information is readily provided, therebyenabling quick diagnosis and resolution of work orders. In some cases,the software may also receive sensor data with the new issue informationassociated with the new issue, and the assigning the new issue to thedata cluster is further performed based on the receive sensor data fromthe asset. In 440, the method includes outputting, to a display, aranking of a plurality of resolutions for a historical issuecorresponding to the assigned data cluster as potential solutions forthe new issue. In some embodiments, the outputting may furtheroutputting similar work orders of historical issues assigned to the datacluster and resolution information for the similar work orders.

FIG. 5 illustrates a computing system 500 for applying subject matterexpert knowledge in accordance with an example embodiment. For example,the computing system 500 may be a cloud platform, a server, a userdevice, or some other computing device with a processor. Also, thedevice 500 may perform the method of FIG. 4. Referring to FIG. 5, thecomputing system 500 includes a network interface 510, a processor 520,an output 530, and a storage device 540. Although not shown in FIG. 5,the computing system 500 may include other components such as a display,an input unit, a receiver/transmitter, and the like. The networkinterface 510 may transmit and receive data over a network such as theInternet, a private network, a public network, and the like. The networkinterface 510 may be a wireless interface, a wired interface, or acombination thereof. The processor 520 may include one or moreprocessing devices each including one or more processing cores. In someexamples, the processor 520 is a multicore processor or a plurality ofmulticore processors. Also, the processor 520 may be fixed or it may bereconfigurable. The output 530 may output data to an embedded display ofthe device 500, an externally connected display, a cloud, anotherdevice, and the like. The storage device 540 is not limited to anyparticular storage device and may include any known memory device suchas RAM, ROM, hard disk, and the like.

According to various embodiments, the network interface 510 may receiveknowledge and issue resolution information developed by one or moresubject matter experts in association with historical issues for a typeof asset. For example, the knowledge and issue resolution informationmay include raw data such as documents, files, orders, parts lists,repair notes, and the like. The processor 520 may generate a pluralityof data clusters for the type of asset based on the received knowledge,wherein each historical issue of the asset is mapped to at least onecluster and includes a plurality of resolutions for the issue. Forexample, the received knowledge for a historical issue may includetextual data, and the processor 520 may scan the text data, extractkeywords from the scanned text data, and assign the historical issue tothe data cluster based on the extracted keywords. In some embodiments,the processor 520 may extract the keywords using a rapid automatickeyword extraction (RAKE) algorithm that is based on a continuoussequence of words. In some embodiments, the processor 520 is alsoconfigured to, for each historical issue, rank the plurality ofrespective resolutions for the historical issue based on the receivedknowledge and the issue resolution information.

The network interface 510 may also receive a new issue including newissue information for the type of asset. The new issue may be an issuethat has yet to be processed by the computing system 500 or theknowledge mapping software described herein, and not necessarily a newissue never seen before by the knowledge mapping software. The processor520 may process the new issue by extracting keywords from the new issueinformation and assigning the new issue to a data cluster from among theplurality of data clusters based on the extracted keywords. In someembodiments, the network interface 510 is further configured to receivesensor data from the asset associated with the new issue, and theprocessor 520 is further configured to assign the new issue to the datacluster based on the receive sensor data from the asset. The output 530may output, to a display, a ranking of a plurality of resolutions for ahistorical issue corresponding to the assigned data cluster as potentialsolutions for the new issue. In some embodiments, the output 530 mayfurther output similar work orders of a historical issue correspondingto the assigned data cluster and resolution information for the similarwork orders.

As will be appreciated based on the foregoing specification, theabove-described examples of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code, may be embodiedor provided within one or more non transitory computer-readable media,thereby making a computer program product, i.e., an article ofmanufacture, according to the discussed examples of the disclosure. Forexample, the non-transitory computer-readable media may be, but is notlimited to, a fixed drive, diskette, optical disk, magnetic tape, flashmemory, semiconductor memory such as read-only memory (ROM), and/or anytransmitting/receiving medium such as the Internet, cloud storage, theinternet of things, or other communication network or link. The articleof manufacture containing the computer code may be made and/or used byexecuting the code directly from one medium, by copying the code fromone medium to another medium, or by transmitting the code over anetwork.

The computer programs (also referred to as programs, software, softwareapplications, “apps”, or code) may include machine instructions for aprogrammable processor, and may be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refer to any computer programproduct, apparatus, cloud storage, internet of things, and/or device(e.g., magnetic discs, optical disks, memory, programmable logic devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The“machine-readable medium” and “computer-readable medium,” however, donot include transitory signals. The term “machine-readable signal”refers to any signal that may be used to provide machine instructionsand/or any other kind of data to a programmable processor.

The above descriptions and illustrations of processes herein should notbe considered to imply a fixed order for performing the process steps.Rather, the process steps may be performed in any order that ispracticable, including simultaneous performance of at least some steps.Although the disclosure has been described in connection with specificexamples, it should be understood that various changes, substitutions,and alterations apparent to those skilled in the art can be made to thedisclosed embodiments without departing from the spirit and scope of thedisclosure as set forth in the appended claims.

What is claimed is:
 1. A computing system comprising a network interfaceconfigured to receive descriptive information about an issue of anasset; and a processor configured to identify keywords from thedescriptive information, map the issue to a cluster from among aplurality of clusters based on the identified keywords, where eachcluster corresponds to a different cause, and output, via a userinterface, information for resolving the issue based on a causecorresponding to the mapped cluster.
 2. The computing system of claim 1,wherein the processor is further configured to determine previousactions taken to resolve similar issues assigned to the cluster, andoutput information about the previous actions via the user interface. 3.The computing system of claim 1, wherein the network interface isfurther configured to receive sensor data of the issue, and theprocessor is further configured to map the issue to the cluster based onthe received sensor data.
 4. The computing system of claim 1, whereinthe cluster represents a cause of failure of the asset from among aplurality of causes of failure of the asset which are represented by theplurality of clusters, respectively.
 5. The computing system of claim 1,wherein each cluster corresponds to a single cause.
 6. The computingsystem of claim 1, wherein the asset comprises one or more of anindustrial machine and an industrial equipment.
 7. The computing systemof claim 1, wherein the processor is configured to display a previousorder that corresponds to an issue of a same cause, and an identifier ofa service technician that resolved the issue.
 8. A method comprisingreceiving descriptive information about an issue of an asset;identifying keywords from the descriptive information; mapping theissue, via a processor, to a cluster from among a plurality of clustersbased on the identified keywords, where each cluster corresponds to adifferent cause; and outputting, via a user interface, information forresolving the issue based on a cause corresponding to the mappedcluster.
 9. The method of claim 8, wherein the method further comprisesdetermining previous actions taken to resolve similar issues assigned tothe cluster, and outputting information about the previous actions viathe user interface.
 10. The method of claim 8, wherein the receivingfurther comprises receiving sensor data of the issue, and the mapping isfurther performed based on the received sensor data.
 11. The method ofclaim 8, wherein the cluster represents a cause of failure of the assetfrom among a plurality of causes of failure of the asset which arerepresented by the plurality of clusters, respectively.
 12. The methodof claim 8, wherein each cluster corresponds to a single cause.
 13. Themethod of claim 8, wherein the asset comprises one or more of anindustrial machine and an industrial equipment.
 14. The method of claim8, wherein the outputting comprises displaying a previous order thatcorresponds to an issue of a same cause, and an identifier of a servicetechnician that resolved the issue.
 15. A non-transitorycomputer-readable medium comprising instructions which when executed bya processor cause a computer to perform a method comprising: receivingdescriptive information about an issue of an asset; identifying keywordsfrom the descriptive information; mapping the issue, via a processor, toa cluster from among a plurality of clusters based on the identifiedkeywords, where each cluster corresponds to a different cause; andoutputting, via a user interface, information for resolving the issuebased on a cause corresponding to the mapped cluster.
 16. Thenon-transitory computer-readable medium of claim 15, wherein the methodfurther comprises determining previous actions taken to resolve similarissues assigned to the cluster, and outputting information about theprevious actions via the user interface.
 17. The non-transitorycomputer-readable medium of claim 15, wherein the receiving furthercomprises receiving sensor data of the issue, and the mapping is furtherperformed based on the received sensor data.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the cluster represents acause of failure of the asset from among a plurality of causes offailure of the asset which are represented by the plurality of clusters,respectively.
 19. The non-transitory computer-readable medium of claim15, wherein each cluster corresponds to a single cause.
 20. Thenon-transitory computer-readable medium of claim 15, wherein theoutputting comprises displaying a previous order that corresponds to anissue of a same cause, and an identifier of a service technician thatresolved the issue.